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Multivariate copula statistical model and weighted sparse classification for radar image target recognition

Ayoub Karine 1 Abdelmalek Toumi 2 Ali Khenchaf 3 Mohammed El Hassouni 4
2 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_ENSTAB_MOM_PIM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : We propose in this paper a new method for targets recognition from radar images. To characterize the radar images, we adopt a statistical multivariate modeling using copula in the complex wavelet domain. For the recognition step, we investigate the weighted sparse representation-based classification (WSRC) method. To build the dictionary, the estimated copula parameters are stacked together in a matrix structure. In order to include the locality information of this dictionary for each unknown radar image to recognize, we affect weights for its atoms (columns). That is done by calculating the Kullback–Leibler divergence (KLD) between the multivariate copula parameters of training and test radar images. Finally, the unknown radar image is recognized through the SRC classifier. Several empirical results carried out on the SAR (synthetic aperture radar) and ISAR (inverse synthetic aperture radar) images demonstrate that the proposed method achieves high recognition rates and outperforms the remaining methods.
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https://hal.archives-ouvertes.fr/hal-02552230
Contributeur : Marie Briec <>
Soumis le : jeudi 23 avril 2020 - 13:48:32
Dernière modification le : mercredi 24 juin 2020 - 16:19:56

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Ayoub Karine, Abdelmalek Toumi, Ali Khenchaf, Mohammed El Hassouni. Multivariate copula statistical model and weighted sparse classification for radar image target recognition. Computers and Electrical Engineering, 2020, 84, pp.106633-1 - 106633-14. ⟨10.1016/j.compeleceng.2020.106633⟩. ⟨hal-02552230⟩

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